RESUMEN
Copy number variation (CNV) has great significance both functionally and evolutionally. Various CNV studies are in progress to find the cause of human disease and to understand the population structure of livestock. Recent advances in next-generation sequencing (NGS) technology have made CNV detection more reliable and accurate at whole-genome level. However, there is a lack of CNV studies on chickens using NGS. Therefore, we obtained whole-genome sequencing data of 65 chickens including Red Jungle Fowl, Cornish (broiler), Rhode Island Red (hybrid), and White Leghorn (layer) from the public databases for CNV region (CNVR) detection. Using CNVnator, a read-depth based software, a total of 663 domesticated-specific CNVRs were identified across autosomes. Gene ontology analysis of genes annotated in CNVRs showed that mainly enriched terms involved in organ development, metabolism, and immune regulation. Population analysis revealed that CN and RIR are closer to each other than WL, and many genes (LOC772271, OR52R1, RD3, ADH6, TLR2B, PRSS2, TPK1, POPDC3, etc.) with different copy numbers between breeds found. In conclusion, this study has helped to understand the genetic characteristics of domestic chickens at CNV level, which may provide useful information for the development of breeding systems in chickens.
RESUMEN
BACKGROUND: Copy Number Variations (CNVs) have becoming very significant variants, representing a major source of genomic variation. CNVs involvement in phenotypic expression and different diseases has been widely demonstrated in humans as well as in many domestic animals. However, genome wide investigation on these structural variations is still missing in Felis catus. The present work is the first CNV mapping from a large data set of Next Generation Sequencing (NGS) data in the domestic cat, performed within the 99 Lives Consortium. RESULTS: Reads have been mapped on the reference assembly_6.2 by Maverix Biomics. CNV detection with cn.MOPS and CNVnator detected 592 CNVs. These CNVs were used to obtain 154 CNV Regions (CNVRs) with BedTools, including 62 singletons. CNVRs covered 0.26% of the total cat genome with 129 losses, 19 gains and 6 complexes. Cluster Analysis and Principal Component Analysis of the detected CNVRs showed that breeds tend to cluster together as well as cats sharing the same geographical origins. The 46 genes identified within the CNVRs were annotated. CONCLUSION: This study has improved the genomic characterization of 14 cat breeds and has provided CNVs information that can be used for studies of traits in cats. It can be considered a sound starting point for genomic CNVs identification in this species.